Systems for digital image compression using context-based pixel predictor selection

Patent metadata

Field	Value
Publication number	US-12002246-B2
Application number	US-202117148928-A
Country	US
Kind code	B2
Filing date	Jan 14, 2021
Priority date	Jan 14, 2021
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

In implementations of systems for digital image compression using context-based pixel predictor selection, a computing device implements a compression system to receive digital image data describing pixels of a digital image. The compression system groups first differences between values of the pixels and first prediction values of the pixels into context groups. A pixel predictor is determined for each of the context groups based on a compression criterion. The compression system generates second prediction values of the pixels using the determined pixel predictor for pixels corresponding to the first differences included in each of the context groups. Second differences between the values of the pixels and the second prediction values of the pixels are grouped into different context groups. The compression system compresses the digital image using entropy coding based on the different context groups.

First claim

What is claimed is:

1. In a digital medium environment for compressing digital images, a method implemented by a computing device, the method comprising:
receiving, by the computing device, digital image data describing pixels of a digital image;
grouping, by the computing device, first differences between values of the pixels and first prediction values of the pixels into context groups;
determining, by the computing device, a pixel predictor for each of the context groups based on a compression criterion;
generating, by the computing device, second prediction values of the pixels using the determined pixel predictor for pixels corresponding to the first differences included in each of the context groups; and
grouping, by the computing device, second differences between the values of the pixels and the second prediction values of the pixels into different context groups.

2. The method as described in claim 1 , further comprising compressing the digital image using entropy coding based on the different context groups.

3. The method as described in claim 2 , wherein compressing the digital image is a lossless compression.

4. The method as described in claim 2 , wherein compressing the digital image is a lossy compression.

5. The method as described in claim 2 , wherein the entropy coding is context adaptive binary arithmetic coding.

6. The method as described in claim 1 , wherein the compression criterion is an average number of bits usable to describe the second differences between the values of the pixels and the second prediction values of the pixels.

7. The method as described in claim 1 , further comprising converting a color space of the pixels from RGB(A) to YCoCg(A) for color decorrelation before grouping the first differences into the context groups.

8. The method as described in claim 1 , wherein grouping the first differences into the context groups is performed using a binary tree.

9. The method as described in claim 1 , wherein grouping the first differences into the context groups is performed using a quadtree.

10. The method as described in claim 1 , wherein grouping the first differences into the context groups is performed using an octree.

11. The method as described in claim 1 , wherein grouping the first differences into the context groups is performed using Gaussian mixture model clustering.

12. The method as described in claim 1 , further comprising:
determining an additional pixel predictor for each of the different context groups based on the compression criterion;
generating third prediction values of the pixels using the determined additional pixel predictor for pixels corresponding to the second differences included in each of the different context groups; and
grouping third differences between the values of the pixels and the third prediction values of the pixels into additional context groups.

13. In a digital medium environment for compressing digital images, a system comprising:
a grouping module implemented at least partially in hardware of a computing device to:
receive digital image data describing pixels of a digital image; and
group first differences between values of the pixels and first prediction values of the pixels into context groups;

a prediction module implemented at least partially in the hardware of the computing device to:
determine a pixel predictor for each of the context groups based on a compression criterion; and
generate second prediction values of the pixels using the determined pixel predictor for pixels corresponding to the first differences included in each of the context groups;

a regrouping module implemented at least partially in the hardware of the computing device to group second differences between the values of the pixels and the second prediction values of the pixels into different context groups; and
an entropy coding module implemented at least partially in the hardware of the computing device to compress the digital image using entropy coding based on the different context groups.

14. The system as described in claim 13 , wherein the compression criterion is an average number of bits usable to describe the second differences between the values of the pixels and the second prediction values of the pixels.

15. The system as described in claim 13 , wherein the entropy coding is context adaptive binary arithmetic coding.

16. The system as described in claim 13 , wherein the grouping module is implemented to group the first differences into the context groups using a binary tree, a quadtree, or an octree.

17. The system as described in claim 13 , wherein the grouping module is implemented to group the first differences into the context groups using Gaussian mixture model clustering.

18. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, causes the computing device to perform operations including:
receiving digital image data describing pixels of a digital image;
generating first prediction values of the pixels;
grouping first differences between values of the pixels and the first prediction values of the pixels into context groups;
determining a pixel predictor for each of the context groups based on a compression criterion;
generating second prediction values of the pixels using the determined pixel predictor for pixels corresponding to the first differences included in each of the context groups; and
grouping second differences between the values of the pixels and the second prediction values of the pixels into different context groups.

19. The one or more computer-readable storage media as described in claim 18 , the operations further including compressing the digital image using entropy coding based on the different context groups.

20. The one or more computer-readable storage media as described in claim 18 , wherein compressing the digital image is a lossless compression.

Assignees

• Adobe Inc

Inventors

• Petrangeli Stefano
• Swaminathan Viswanathan
• Wang Haoliang

Classifications

• G06T9/40 Primary

  Tree coding, e.g. quadtree, octree · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06T3/40

  Scaling of whole images or parts thereof, e.g. expanding or contracting · CPC title

• H04N19/13 Primary

  Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC] · CPC title

• H04N19/186

  the unit being a colour or a chrominance component · CPC title